Amelioration of Parkinsonian tremor evoked by DBS: which role play cerebello-(sub)thalamic fiber tracts?

Abstract

Background Current pathophysiological models of Parkinson’s disease (PD) assume a malfunctioning network being adjusted by the DBS signal. As various authors showed a main involvement of the cerebellum within this network, cerebello-cerebral fiber tracts are gaining special interest regarding the mediation of DBS effects. Objectives The crossing and non-decussating fibers of the dentato-rubro-thalamic tract (c-DRTT/nd-DRTT) and the subthalamo-ponto-cerebellar tract (SPCT) are thought to build up an integrated network enabling a bidimensional communication between the cerebellum and the basal ganglia. The aim of this study was to investigate the influence of these tracts on clinical control of Parkinsonian tremor evoked by DBS. Methods We analyzed 120 electrode contacts from a cohort of 14 patients with tremor-dominant or equivalence-type PD having received bilateral STN-DBS. Probabilistic tractography was performed to depict the c-DRTT, nd-DRTT, and SPCT. Distance maps were calculated for the tracts and correlated to clinical tremor control for each electrode pole. Results A significant difference between “effective” and “less-effective” contacts was only found for the c-DRTT (p = 0.039), but not for the SPCT, nor the nd-DRTT. In logistic and linear regressions, significant results were also found for the c-DRTT only (pmodel logistic = 0.035, ptract logistic = 0,044; plinear = 0.027). Conclusions We found a significant correlation between the distance of the DBS electrode pole to the c-DRTT and the clinical efficacy regarding tremor reduction. The c-DRTT might therefore play a major role in the mechanisms of alleviation of Parkinsonian tremor and could eventually serve as a possible DBS target for tremor-dominant PD in future.

A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning

“…The cerebellum, as a brain region associated with motor functions, may reveal microstructural alterations related to PD through changes in its texture features. Considering the cerebellum's role in coordination and fine motor control, investigating the texture features of gray and white matter greatly contributes to a profound understanding of PD's neurodegenerative process (Deuter et al, 2023 ; Iskusnykh et al, 2024 ; Saban et al, 2024 ). The purpose of this paper is not only to explore the potential of Parkinson's disease diagnosis using machine learning with cerebellar gray matter and white matter texture features, but also to find the most diagnostically valuable factors through model visualization.…”